Pain rehabilitation: E/Motion-based automated coaching
Lead Research Organisation:
Imperial College London
Department Name: Computing
Abstract
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Organisations
People |
ORCID iD |
| Maja Pantic (Principal Investigator) |
Publications
Chugh A
(2023)
Comparative docking studies of drugs and phytocompounds for emerging variants of SARS-CoV-2.
in 3 Biotech
Zafeiriou S
(2015)
A survey on face detection in the wild: Past, present and future
in Computer Vision and Image Understanding
Aung MSH
(2016)
The Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges and the Multimodal EmoPain Dataset.
in IEEE transactions on affective computing
Jiang B
(2014)
A dynamic appearance descriptor approach to facial actions temporal modeling.
in IEEE transactions on cybernetics
Georgakis C
(2016)
Discriminant Incoherent Component Analysis
in IEEE Transactions on Image Processing
Zafeiriou L
(2017)
Nonnegative Decompositions for Dynamic Visual Data Analysis.
in IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Antonakos E
(2015)
Feature-based Lucas-Kanade and active appearance models.
in IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Liwicki S
(2015)
Online kernel slow feature analysis for temporal video segmentation and tracking.
in IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Tzimiropoulos G
(2014)
Active Orientation Models for Face Alignment In-the-Wild
in IEEE Transactions on Information Forensics and Security
Papaioannou A
(2014)
Principal Component Analysis With Complex Kernel: The Widely Linear Model
in IEEE Transactions on Neural Networks and Learning Systems
| Description | (a) Pain intensity, as shown in rehabilitation-related scenarios, can be automatically estimated from facial expressions with high Pearson correlation coefficient (CORR >= 0.5). This can be done either by firstly recognising facial actions (i.e. facial action units) underlying the expression of pain, or by estimating the intensity of facial expression of pain directly from the extent of changes in facial features such as the displacement of facial characteristic points. (b) The best results are achieved if accurate facial point trackers are used and facial point locations and displacements are used to represent changes in the observed facial expressions. (c) Discriminative machine learning approaches perform robustly for the target problem (i.e. pain intensity estimation) but cannot handle missing data, which is typical in real-world scenarios as occlusions and self-occlussions often occur. For this problem, it has been shown that a generative approach (i.e. newly-proposed Latent Trees) has a superior performance. |
| Exploitation Route | Some of the developed methodologies are publicly available in http://ibug.doc.ic.ac.uk/resources |
| Sectors | Digital/Communication/Information Technologies (including Software),Healthcare |
| URL | http://www.uclic.ucl.ac.uk/people/n.berthouze/EPain.html |
| Description | The consortium collected a large database of multimodal recordings of human behaviour in rehabilitation scenario in which they experienced pain while performing rehabilitation exercises. The database has been properly documented, annotated in terms of pain level as judged by human experts, and released according to ethical clearance guidelines. This database has a very large potential impact as it allows academics and scientists all over the world to study the problem of pain estimation by humans and machines based on various signals including facial expressions captured at a very high frequency and resolution. |
| First Year Of Impact | 2015 |
| Sector | Digital/Communication/Information Technologies (including Software),Healthcare |
| Impact Types | Societal |